Note
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Default settings.¶
from __future__ import annotations
from gemseo import configure
from gemseo import configure_logger
from gemseo import sample_disciplines
from gemseo.mlearning.regression.algos.ot_gpr import OTGaussianProcessRegressor
from gemseo.uncertainty.statistics.empirical_statistics import EmpiricalStatistics
from gemseo_mlearning.active_learning.active_learning_algo import ActiveLearningAlgo
from gemseo_mlearning.problems.rosenbrock.rosenbrock_discipline import (
RosenbrockDiscipline,
)
from gemseo_mlearning.problems.rosenbrock.rosenbrock_space import RosenbrockSpace
# Update the configuration of |g| to speed up the script (use configure() with care)
configure(False, False, True, False, False, False, False)
configure_logger()
Out:
<RootLogger root (INFO)>
The use of active learning methods dedicated to quantile estimation is illustrated in this example, with all default settings. The function with the quantile of interest is the Rosenbrock function \(f(x_1,x_2)=(1-x_1)^2+100(x_2-x_1^2)^2\):
discipline = RosenbrockDiscipline()
with \(x_1\) and \(x_2\) uniformly distributed over \([-2,2]^2\):
uncertain_space = RosenbrockSpace()
First, we create an initial training dataset using an optimal LHS including 10 samples:
learning_dataset = sample_disciplines(
[discipline], uncertain_space, "y", algo_name="OT_OPT_LHS", n_samples=10
)
Out:
WARNING - 05:20:39: No coupling in MDA, switching chain_linearize to True.
INFO - 05:20:39: *** Start Sampling execution ***
INFO - 05:20:39: Sampling
INFO - 05:20:39: Disciplines: RosenbrockDiscipline
INFO - 05:20:39: MDO formulation: MDF
INFO - 05:20:39: Running the algorithm OT_OPT_LHS:
INFO - 05:20:39: 10%|█ | 1/10 [00:00<00:00, 4490.69 it/sec]
INFO - 05:20:39: 20%|██ | 2/10 [00:00<00:00, 4211.15 it/sec]
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INFO - 05:20:39: 100%|██████████| 10/10 [00:00<00:00, 4722.25 it/sec]
INFO - 05:20:39: *** End Sampling execution ***
and an initial Gaussian process regressor from OpenTURNS:
regressor = OTGaussianProcessRegressor(learning_dataset, trend="quadratic")
Then, we look for 20 points that will help us to approximate the 35% quantile. By default, for this purpose, the active learning algorithm looks for the point minimizing the U-function with the help of the SLSQP algorithm applied in a multistart framework.
level = 0.35
active_learning = ActiveLearningAlgo(
"Quantile",
uncertain_space,
regressor,
level=level,
uncertain_space=uncertain_space,
)
active_learning.acquire_new_points(discipline, 20)
Out:
INFO - 05:20:39: Acquiring 20 points in batches of 1
INFO - 05:20:40: 5%|5 | 1/20 [00:00<00:08, 2.27 it/sec]
/builds/gemseo/dev/gemseo-mlearning/src/gemseo_mlearning/active_learning/acquisition_criteria/level_set/u.py:71: RuntimeWarning: divide by zero encountered in divide
abs(self._output_value - self._compute_mean(input_value))
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/builds/gemseo/dev/gemseo-mlearning/src/gemseo_mlearning/active_learning/acquisition_criteria/level_set/u.py:71: RuntimeWarning: divide by zero encountered in divide
abs(self._output_value - self._compute_mean(input_value))
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/builds/gemseo/dev/gemseo-mlearning/src/gemseo_mlearning/active_learning/acquisition_criteria/level_set/u.py:71: RuntimeWarning: divide by zero encountered in divide
abs(self._output_value - self._compute_mean(input_value))
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/builds/gemseo/dev/gemseo-mlearning/src/gemseo_mlearning/active_learning/acquisition_criteria/level_set/u.py:71: RuntimeWarning: divide by zero encountered in divide
abs(self._output_value - self._compute_mean(input_value))
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INFO - 05:20:45: 60%|###### | 12/20 [00:05<00:03, 2.13 it/sec]
/builds/gemseo/dev/gemseo-mlearning/src/gemseo_mlearning/active_learning/acquisition_criteria/level_set/u.py:71: RuntimeWarning: divide by zero encountered in divide
abs(self._output_value - self._compute_mean(input_value))
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/builds/gemseo/dev/gemseo-mlearning/src/gemseo_mlearning/active_learning/acquisition_criteria/level_set/u.py:71: RuntimeWarning: divide by zero encountered in divide
abs(self._output_value - self._compute_mean(input_value))
INFO - 05:20:46: 75%|#######5 | 15/20 [00:07<00:02, 2.13 it/sec]
/builds/gemseo/dev/gemseo-mlearning/src/gemseo_mlearning/active_learning/acquisition_criteria/level_set/u.py:71: RuntimeWarning: divide by zero encountered in divide
abs(self._output_value - self._compute_mean(input_value))
INFO - 05:20:47: 80%|######## | 16/20 [00:07<00:01, 2.12 it/sec]
INFO - 05:20:47: 85%|########5 | 17/20 [00:08<00:01, 2.11 it/sec]
/builds/gemseo/dev/gemseo-mlearning/src/gemseo_mlearning/active_learning/acquisition_criteria/level_set/u.py:71: RuntimeWarning: divide by zero encountered in divide
abs(self._output_value - self._compute_mean(input_value))
INFO - 05:20:48: 90%|######### | 18/20 [00:08<00:01, 2.09 it/sec]
INFO - 05:20:48: 95%|#########5| 19/20 [00:09<00:00, 2.08 it/sec]
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INFO - 05:20:49: 100%|##########| 20/20 [00:09<00:00, 2.08 it/sec]
(<gemseo.algos.database.Database object at 0x7ca013d28800>, Optimization problem:
minimize U
with respect to x)
Then, we plot the history of the quantity of interest
active_learning.plot_qoi_history()
Out:
<gemseo.post.dataset.lines.Lines object at 0x7ca013b07050>
as well as the training points, the original model, the Gaussian process regressor and the U-function after the last acquisition:
active_learning.plot_acquisition_view(discipline=discipline)
Out:
<Figure size 640x480 with 7 Axes>
Finally, we compare the estimated quantile from the active learning procedure to the Monte Carlo estimate for both algorithms:
dataset = sample_disciplines(
[discipline], uncertain_space, "y", algo_name="OT_MONTE_CARLO", n_samples=10000
)
reference_quantile = EmpiricalStatistics(dataset, ["y"]).compute_quantile(level)
Out:
WARNING - 05:20:49: No coupling in MDA, switching chain_linearize to True.
INFO - 05:20:49: *** Start Sampling execution ***
INFO - 05:20:49: Sampling
INFO - 05:20:49: Disciplines: RosenbrockDiscipline
INFO - 05:20:49: MDO formulation: MDF
INFO - 05:20:49: Running the algorithm OT_MONTE_CARLO:
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INFO - 05:20:51: *** End Sampling execution ***
Total running time of the script: ( 0 minutes 32.931 seconds)
Download Python source code: plot_quantile.py